Amazon EMR vs Hadoop: What are the differences?

Introduction

Amazon EMR (Elastic MapReduce) and Hadoop are both technologies used for processing and analyzing large datasets. While they share similarities, there are key differences between the two.

1. Data Storage: One significant difference between Amazon EMR and Hadoop is the data storage. In Hadoop, data is stored in the Hadoop Distributed File System (HDFS). On the other hand, Amazon EMR allows you to choose from various data storage options like Amazon S3, HDFS, or a combination of both. This flexibility allows users to leverage existing storage infrastructure or use cost-effective cloud storage.

2. Managed Service: Amazon EMR is a managed service provided by AWS, which means that Amazon takes care of the infrastructure and administration tasks such as setup, patching, and monitoring. In contrast, Hadoop is an open-source framework that requires users to set up and manage their own Hadoop clusters. This key difference makes Amazon EMR a more convenient and hassle-free option for users who prefer a fully managed service.

3. Ease of Use: Another difference between Amazon EMR and Hadoop is the ease of use. Amazon EMR offers a user-friendly web interface and command-line tools that simplify the process of managing and monitoring clusters. It provides pre-configured applications like Apache Spark, Apache Hive, and Apache Zeppelin, making it easier for users to start processing their data quickly. Hadoop, on the other hand, requires users to have a deeper understanding of the technology and often involves more manual configuration and setup.

4. Integration with AWS Services: One advantage of Amazon EMR is its seamless integration with other AWS services. With Amazon EMR, users can easily integrate with services like Amazon Redshift for data warehousing, Amazon Machine Learning for predictive analytics, or Amazon Athena for interactive query analysis. Hadoop, being an open-source framework, requires additional effort for integrating with AWS services, making Amazon EMR a more integrated and well-supported option.

5. Automated Scaling: Amazon EMR offers automated scaling capabilities, allowing users to add or remove instances in the cluster based on the workload. This automated scaling helps optimize resource usage and reduce costs by automatically scaling the cluster up or down based on demand. While Hadoop also provides scaling capabilities, it requires more manual intervention and management compared to the automated scaling offered by Amazon EMR.

6. Cost: Lastly, the cost structure is different between Amazon EMR and Hadoop. Hadoop is open-source and free to use, but it requires users to bear the cost of infrastructure setup, maintenance, and scaling. On the other hand, Amazon EMR has a pay-as-you-go pricing model, where users pay for the resources they use, making it a more flexible and cost-effective option in terms of managing large data processing workloads.

In Summary, Amazon EMR and Hadoop differ in terms of data storage options, managed service offering, ease of use, integration with other AWS services, automated scaling capabilities, and cost structure.